This paper provides a thorough review of the developments in deep learning-based multimodal fusion for medical classification tasks. The authors explore the complementary relationships among prevalent clinical modalities and outline three main fusion schemes for multimodal classification networks: input fusion, intermediate fusion (encompassing single-level fusion, hierarchical fusion, and attention-based fusion), and output fusion.
By evaluating the performance of these fusion techniques, the authors provide insight into the suitability of different network architectures for various multimodal fusion scenarios and application domains. Furthermore, they delve into challenges related to network architecture selection, handling incomplete multimodal data management, and the potential limitations of multimodal fusion. Finally, the authors spotlight the promising future of Transformer-based multimodal fusion techniques and give recommendations for future research in this rapidly evolving field.
Naar een andere taal
vanuit de broninhoud
arxiv.org
Belangrijkste Inzichten Gedestilleerd Uit
by Yiha... om arxiv.org 04-24-2024
https://arxiv.org/pdf/2404.15022.pdfDiepere vragen